The situation
AI agents were arriving faster than the enterprise could govern them. Without shared architecture, every business unit would build its own stack — duplicating spend, fragmenting security, and making agent quality impossible to manage at Fortune 25 scale.
The company needed the foundation before the sprawl: one way to access models, one way to connect tools and context, and one way to know what agents exist and what they cost.
What we did
- 01Designed the enterprise agentic-AI architecture and operating framework end to end.
- 02Stood up a central LLM gateway governing model access, routing, and policy across providers.
- 03Established an MCP runtime as the standard for connecting agents to tools, data, and context.
- 04Built a skill and agent registry so agents are discoverable, reusable, and governed rather than duplicated.
- 05Implemented AI FinOps — cost visibility and controls across every model call and agent.
- 06Launched an AI agent-building factory with best-practice sharing, so business units ship agents on shared rails instead of reinventing the stack.
- 07Developed an enterprise-ready agentic SDLC: eleven specialized AI agents running the software lifecycle end to end — capture, design, build, verify, operate — with test-first discipline built in.
- 08Put a cross-model independent judge on every agent output — a different frontier model grades the work, avoiding shared blind spots.
- 09Backed the SDLC agents with a shared context-graph memory linking the repo, meeting records, PRDs, and tickets — and wired production bugs back in as new requirements.
The outcome
- A production agentic-AI platform the whole enterprise builds on — gateway, runtime, registry, and FinOps in place.
- A repeatable factory model: new agents ship on shared rails with governance and cost discipline from day one.
- Software delivery running through the agentic SDLC — faster, cheaper, and higher quality, with an independent judge on every output.
- Best practices flow across business units instead of being relearned in each one.